Setup

In [1]:

Code


# To track render duration
start_time <- Sys.time()

# set width of console output
options(width = 80)


# Install and load required packages
p_required <- c(
  "tidyverse",
  "here",
  "janitor",
  "knitr",
  "dagitty",
  "ggdag",
  "ggpubr",
  "gtsummary",
  "gt",
  "scales",
  "sessioninfo"
)
packages <- rownames(installed.packages())
p_to_install <- p_required[!(p_required %in% packages)]
if (length(p_to_install) > 0) {
  install.packages(p_to_install)
}
sapply(p_required, require, character.only = TRUE)

Loading required package: tidyverse

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.6
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.1     ✔ tibble    3.3.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.2
✔ purrr     1.2.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Loading required package: here

here() starts at C:/R/research/CSAP

Loading required package: janitor


Attaching package: 'janitor'


The following objects are masked from 'package:stats':

    chisq.test, fisher.test


Loading required package: knitr

Loading required package: dagitty

Loading required package: ggdag


Attaching package: 'ggdag'


The following object is masked from 'package:stats':

    filter


Loading required package: ggpubr

Loading required package: gtsummary

Loading required package: gt

Loading required package: scales


Attaching package: 'scales'


The following object is masked from 'package:purrr':

    discard


The following object is masked from 'package:readr':

    col_factor


Loading required package: sessioninfo

  tidyverse        here     janitor       knitr     dagitty       ggdag 
       TRUE        TRUE        TRUE        TRUE        TRUE        TRUE 
     ggpubr   gtsummary          gt      scales sessioninfo 
       TRUE        TRUE        TRUE        TRUE        TRUE

Code


rm(p_required, p_to_install, packages)

In [2]:

Code


eu25games2019 <- readRDS(
  file = here(
    "data",
    "03_final",
    "eu25games2019.rds"
  )
)

Distribution of implicit and explicit partisans pooled and across countries

In [3]:

Code


eu25games2019 |>
  tabyl(der_pid) |>
  adorn_totals() |>
  kable(
    digits = 2,
    booktabs = T
  )

In [4]:

Distribution of reported partisan attachement (explicit partisan identity) in the pooled sample.

der_pid	n	percent
Party ID reported	110142	0.62
No Party ID	65280	0.36
Other	3276	0.02
NA	238	0.00
Total	178936	1.00

In [5]:

Code


eu25games2019 |>
  tabyl(der_vote_cat) |>
  adorn_totals() |>
  kable(
    digits = 2,
    booktabs = T
  )

In [6]:

Distribution of reported vote intention and choice (implicit partisan identity) in the pooled sample.

der_vote_cat	n	percent
Vote reported	140384	0.78
Don’t know	29920	0.17
Other	8046	0.04
NA	586	0.00
Total	178936	1.00

In [7]:

Code


eu25games2019 |>
  tabyl(der_pid, der_vote_cat) |>
  adorn_totals() |>
  kable(
    digits = 2,
    booktabs = T
  )

In [8]:

Distribution of explicit and implicit partisan identities in the pooled sample.

der_pid	Vote reported	Don’t know	Other	NA
Party ID reported	101502	6180	2172	288
No Party ID	37404	23400	4398	78
Other	1464	336	1476	0
NA	14	4	0	220
Total	140384	29920	8046	586

In [9]:

Code


eu25games2019 |>
  tabyl(meta_country, der_pid) |>
  adorn_totals() |>
  kable(
    digits = 2,
    booktabs = T
  )

In [10]:

Distribution of reported partisan attachement (explicit partisan identity) by country.

meta_country	Party ID reported	No Party ID	Other	NA
Austria	3978	3600	78	6
Belgium	5292	2454	78	6
Bulgaria	2790	2916	180	6
Croatia	3786	3294	354	6
Czech Republic	4848	1866	84	12
Denmark	6006	1032	156	6
Estonia	3318	2256	78	12
Finland	4362	2418	174	6
France	4446	2454	18	18
Germany	4062	3000	66	0
Greece	4194	2304	462	6
Hungary	3834	1986	96	0
Ireland	3084	3192	84	6
Italy	5346	1602	66	18
Latvia	2676	4092	114	6
Lithuania	3348	4038	204	0
Netherlands	5028	2226	60	12
Poland	5190	1956	36	6
Portugal	4386	2574	144	10
Romania	4932	3744	168	18
Slovakia	4536	3114	132	0
Slovenia	4278	2376	120	36
Spain	5934	2274	156	12
Sweden	5622	1758	132	12
United Kingdom	4866	2754	36	18
Total	110142	65280	3276	238

In [11]:

Code


eu25games2019 |>
  tabyl(der_vote_cat, meta_country) |>
  adorn_totals() |>
  kable(
    digits = 2,
    booktabs = T
  )

In [12]:

Distribution of reported vote intention and choice (implicit partisan identity) in the pooled sample.

der_vote_cat	Austria	Belgium	Bulgaria	Croatia	Czech Republic	Denmark	Estonia	Finland	France	Germany	Greece	Hungary	Ireland	Italy	Latvia	Lithuania	Netherlands	Poland	Portugal	Romania	Slovakia	Slovenia	Spain	Sweden	United Kingdom
Vote reported	6444	6498	4182	4548	5082	6174	4908	5412	5292	5436	5310	4824	4980	5592	5064	5994	5904	6222	4770	6992	5808	5076	7152	6288	6432
Don’t know	1098	1110	1104	1914	1518	858	396	1188	1386	1044	1182	882	1152	1272	1248	1332	1224	930	1738	1350	1668	1296	996	972	1062
Other	108	210	600	972	192	156	348	330	204	408	462	210	228	150	570	264	186	30	606	504	306	402	204	252	144
NA	12	12	6	6	18	12	12	30	54	240	12	0	6	18	6	0	12	6	0	16	0	36	24	12	36
Total	7662	7830	5892	7440	6810	7200	5664	6960	6936	7128	6966	5916	6366	7032	6888	7590	7326	7188	7114	8862	7782	6810	8376	7524	7674

Code


eu25games2019 |>
  tabyl(der_pid, der_vote_cat, meta_country) |>
  adorn_totals()

In [13]:

Distribution of explicit and implicit partisan identities by country.

$Austria
           der_pid Vote reported Don't know Other NA
 Party ID reported          3774        192     6  6
       No Party ID          2622        900    78  0
             Other            48          6    24  0
                NA             0          0     0  6
             Total          6444       1098   108 12

$Belgium
           der_pid Vote reported Don't know Other NA
 Party ID reported          5040        210    36  6
       No Party ID          1434        888   132  0
             Other            24         12    42  0
                NA             0          0     0  6
             Total          6498       1110   210 12

$Bulgaria
           der_pid Vote reported Don't know Other NA
 Party ID reported          2520        168   102  0
       No Party ID          1548        918   450  0
             Other           114         18    48  0
                NA             0          0     0  6
             Total          4182       1104   600  6

$Croatia
           der_pid Vote reported Don't know Other NA
 Party ID reported          3102        348   336  0
       No Party ID          1320       1554   420  0
             Other           126         12   216  0
                NA             0          0     0  6
             Total          4548       1914   972  6

$`Czech Republic`
           der_pid Vote reported Don't know Other NA
 Party ID reported          4236        546    60  6
       No Party ID           828        966    72  0
             Other            18          6    60  0
                NA             0          0     0 12
             Total          5082       1518   192 18

$Denmark
           der_pid Vote reported Don't know Other NA
 Party ID reported          5568        360    72  6
       No Party ID           540        444    48  0
             Other            66         54    36  0
                NA             0          0     0  6
             Total          6174        858   156 12

$Estonia
           der_pid Vote reported Don't know Other NA
 Party ID reported          3120         72   126  0
       No Party ID          1728        324   204  0
             Other            60          0    18  0
                NA             0          0     0 12
             Total          4908        396   348 12

$Finland
           der_pid Vote reported Don't know Other NA
 Party ID reported          4068        204    72 18
       No Party ID          1284        972   156  6
             Other            60         12   102  0
                NA             0          0     0  6
             Total          5412       1188   330 30

$France
           der_pid Vote reported Don't know Other NA
 Party ID reported          4176        180    54 36
       No Party ID          1110       1194   150  0
             Other             6         12     0  0
                NA             0          0     0 18
             Total          5292       1386   204 54

$Germany
           der_pid Vote reported Don't know Other  NA
 Party ID reported          3666         90   132 174
       No Party ID          1764        948   222  66
             Other             6          6    54   0
                NA             0          0     0   0
             Total          5436       1044   408 240

$Greece
           der_pid Vote reported Don't know Other NA
 Party ID reported          3894        222    72  6
       No Party ID          1194        912   198  0
             Other           222         48   192  0
                NA             0          0     0  6
             Total          5310       1182   462 12

$Hungary
           der_pid Vote reported Don't know Other NA
 Party ID reported          3642        168    24  0
       No Party ID          1134        708   144  0
             Other            48          6    42  0
                NA             0          0     0  0
             Total          4824        882   210  0

$Ireland
           der_pid Vote reported Don't know Other NA
 Party ID reported          2916        120    48  0
       No Party ID          2016       1032   144  0
             Other            48          0    36  0
                NA             0          0     0  6
             Total          4980       1152   228  6

$Italy
           der_pid Vote reported Don't know Other NA
 Party ID reported          4950        366    30  0
       No Party ID           618        900    84  0
             Other            24          6    36  0
                NA             0          0     0 18
             Total          5592       1272   150 18

$Latvia
           der_pid Vote reported Don't know Other NA
 Party ID reported          2346        174   156  0
       No Party ID          2652       1062   378  0
             Other            66         12    36  0
                NA             0          0     0  6
             Total          5064       1248   570  6

$Lithuania
           der_pid Vote reported Don't know Other NA
 Party ID reported          3132        192    24  0
       No Party ID          2724       1122   192  0
             Other           138         18    48  0
                NA             0          0     0  0
             Total          5994       1332   264  0

$Netherlands
           der_pid Vote reported Don't know Other NA
 Party ID reported          4704        270    54  0
       No Party ID          1182        948    96  0
             Other            18          6    36  0
                NA             0          0     0 12
             Total          5904       1224   186 12

$Poland
           der_pid Vote reported Don't know Other NA
 Party ID reported          4950        240     0  0
       No Party ID          1242        690    24  0
             Other            30          0     6  0
                NA             0          0     0  6
             Total          6222        930    30  6

$Portugal
           der_pid Vote reported Don't know Other NA
 Party ID reported          3672        498   216  0
       No Party ID          1068       1218   288  0
             Other            24         18   102  0
                NA             6          4     0  0
             Total          4770       1738   606  0

$Romania
           der_pid Vote reported Don't know Other NA
 Party ID reported          4614        186   126  6
       No Party ID          2316       1128   300  0
             Other            54         36    78  0
                NA             8          0     0 10
             Total          6992       1350   504 16

$Slovakia
           der_pid Vote reported Don't know Other NA
 Party ID reported          4104        306   126  0
       No Party ID          1632       1350   132  0
             Other            72         12    48  0
                NA             0          0     0  0
             Total          5808       1668   306  0

$Slovenia
           der_pid Vote reported Don't know Other NA
 Party ID reported          3798        348   132  0
       No Party ID          1224        942   210  0
             Other            54          6    60  0
                NA             0          0     0 36
             Total          5076       1296   402 36

$Spain
           der_pid Vote reported Don't know Other NA
 Party ID reported          5688        186    54  6
       No Party ID          1386        786    96  6
             Other            78         24    54  0
                NA             0          0     0 12
             Total          7152        996   204 24

$Sweden
           der_pid Vote reported Don't know Other NA
 Party ID reported          5274        270    78  0
       No Party ID           966        696    96  0
             Other            48          6    78  0
                NA             0          0     0 12
             Total          6288        972   252 12

$`United Kingdom`
           der_pid Vote reported Don't know Other NA
 Party ID reported          4548        264    36 18
       No Party ID          1872        798    84  0
             Other            12          0    24  0
                NA             0          0     0 18
             Total          6432       1062   144 36

Overview of data distribution

Respondents -> Party ID -> Vote report

In words, respondents either report a partisan identity (i.e., a felt attachement to a political party) or they deny the presence of such feelings.

The number of respondents

Code


eu25games2019 |>
  distinct(meta_pid) |>
  nrow()

In [14]:

[1] 29827

The numer of respondents reporting PID vs not

Code


eu25games2019 |>
  distinct(meta_pid, .keep_all = T) |>
  count(der_pid)

In [15]:

# A tibble: 4 × 2
  der_pid               n
  <fct>             <int>
1 Party ID reported 18357
2 No Party ID       10880
3 Other               546
4 NA                   44

The number of non-identifiers who report a vote (vs not)

Code


eu25games2019 |>
  distinct(meta_pid, .keep_all = T) |>
  filter(der_pid == "No Party ID") |>
  count(der_vote_cat)

In [16]:

# A tibble: 4 × 2
  der_vote_cat      n
  <fct>         <int>
1 Vote reported  6234
2 Don't know     3900
3 Other           733
4 NA               13

The number of identifiers who report a vote (vs not)

Code


eu25games2019 |>
  distinct(meta_pid, .keep_all = T) |>
  filter(der_pid == "Party ID reported") |>
  count(der_vote_cat)

In [17]:

# A tibble: 4 × 2
  der_vote_cat      n
  <fct>         <int>
1 Vote reported 16917
2 Don't know     1030
3 Other           362
4 NA               48

The number of identifiers who reported vote identical to PID

Code


eu25games2019 |>
  distinct(meta_pid, .keep_all = T) |>
  filter(der_pid == "Party ID reported") |>
  mutate(
    pid_equal_vote_name = if_else(
      der_vote_combined_name == ext_q_party_id_pf_name,
      1,
      0
    )
  ) |>
  count(pid_equal_vote_name)

Code


# but accounting for unknown and dont-know answers yields
eu25games2019 |>
  distinct(meta_pid, .keep_all = T) |>
  filter(
    der_pid == "Party ID reported",
    der_vote_combined_name != "unknown",
    der_vote_combined_name != "dont-know"
  ) |>
  mutate(
    pid_equal_vote_name = if_else(
      der_vote_combined_name == ext_q_party_id_pf_name,
      1,
      0
    ),
    pid_equal_vote_id = if_else(
      der_vote_combined_id == ext_q_party_id_pf_id,
      1,
      0
    )
  ) |>
  count(pid_equal_vote_name, pid_equal_vote_id)

In [18]:

# A tibble: 3 × 2
  pid_equal_vote_name     n
                <dbl> <int>
1                   0  5858
2                   1 12451
3                  NA    48

# A tibble: 4 × 3
  pid_equal_vote_name pid_equal_vote_id     n
                <dbl>             <dbl> <int>
1                   0                 0  3626
2                   0                NA  1202
3                   1                 1 12437
4                   1                NA    14

Code


eu25games2019 |>
  distinct(meta_pid, .keep_all = T) |>
  filter(der_pid == "Party ID reported") |>
  mutate(
    pid_equal_vote_id = if_else(
      der_vote_combined_id == ext_q_party_id_pf_id,
      1,
      0
    )
  ) |>
  count(pid_equal_vote_id)

In [19]:

# A tibble: 3 × 2
  pid_equal_vote_id     n
              <dbl> <int>
1                 0  3626
2                 1 12437
3                NA  2294

In [20]:

Code

eu25games2019 <- eu25games2019 |>
  mutate(
    pid_equal_vote_id = case_when(
      der_pid == "Party ID reported" &
        der_vote_cat == "Vote reported" &
        der_vote_combined_id == ext_q_party_id_pf_id ~ 1,
      der_pid == "Party ID reported" &
        der_vote_cat == "Vote reported" &
        der_vote_combined_id != ext_q_party_id_pf_id ~ 0,
      .default = NA
    ),
    pid_equal_vote_name = case_when(
      der_pid == "Party ID reported" &
        der_vote_cat == "Vote reported" &
        der_vote_combined_name == ext_q_party_id_pf_name ~ 1,
      der_pid == "Party ID reported" &
        der_vote_cat == "Vote reported" &
        der_vote_combined_name != ext_q_party_id_pf_name ~ 0,
      .default = NA
    )
  )

# manually recode some instances where the casewhen check above yielded different but inspection revealed its basically the same party
eu25games2019 <- eu25games2019 |>
  mutate(
    # New column using case_when for manual line-by-line recoding
    pid_equal_vote_name2 = case_when(
      # Case 1: Party names are already identical (from pid_equal_vote_name = 1)
      pid_equal_vote_name == 1 ~ 1,

      # Case 2: Polish Koalicja Obywatelska (PO, SLD, Spring, PSL, Nowoczesna)
      pid_equal_vote_name == 0 &
        ext_q_party_id_pf_name %in%
          c(
            "Platforma Obywatelska",
            "Sojusz Lewicy Demokratycznej",
            "Spring",
            "Polskie Stronnictwo Ludowe",
            "Nowoczesna"
          ) &
        der_vote_combined_name == "Koalicja Obywatelska" ~ 1,

      # Case 3: Spanish Unidas Podemos (Podemos, Izquierda Unida)
      pid_equal_vote_name == 0 &
        ext_q_party_id_pf_name %in%
          c(
            "Podemos",
            "Izquierda Unida"
          ) &
        der_vote_combined_name == "Unidas Podemos" ~ 1,

      # Case 4: Dutch ChristenUnie — SGP
      pid_equal_vote_name == 0 &
        ext_q_party_id_pf_name %in%
          c(
            "ChristenUnie",
            "Staatkundig Gereformeerde Partij"
          ) &
        der_vote_combined_name ==
          "ChristenUnie — Staatkundig Gereformeerde Partij" ~ 1,

      # Case 5: Hungarian Fidesz — KDNP (using grepl for robustness against long name)
      pid_equal_vote_name == 0 &
        ext_q_party_id_pf_name %in%
          c(
            "Fidesz — Magyar Polgári Szövetség",
            "Kereszténydemokrata Néppárt"
          ) &
        grepl(
          "Fidesz — KDNP pártszövetség",
          der_vote_combined_name,
          fixed = TRUE
        ) ~ 1,

      # Case 6: Polish Konfederacja Wolność i Niepodległość (KNP, KORWiN, Kukiz'15)
      pid_equal_vote_name == 0 &
        ext_q_party_id_pf_name %in%
          c(
            "Kongres Nowej Prawicy",
            "Koalicja Odnowy Rzeczypospolitej Wolność i Nadzieja KORWiN",
            "Kukiz'15"
          ) &
        der_vote_combined_name == "Konfederacja Wolność i Niepodległość" ~ 1,

      # Case 7: Latvian Attīstībai/Par!
      pid_equal_vote_name == 0 &
        ext_q_party_id_pf_name == "Kustība Par!" &
        der_vote_combined_name == "Attīstībai/Par!" ~ 1,

      # Case 8: French LREM/Renaissance
      pid_equal_vote_name == 0 &
        ext_q_party_id_pf_name == "Mouvement démocrate" &
        der_vote_combined_name == "La République En Marche! / Renaissance" ~ 1,

      # Case 9: Catalan JxCat (Partit Demòcrata Europeu Català)
      pid_equal_vote_name == 0 &
        ext_q_party_id_pf_name == "Partit Demòcrata Europeu Català" &
        der_vote_combined_name ==
          "Junts per Catalunya — Partit Demòcrata Europeu Català" ~ 1,

      # Default: All other cases (where pid_equal_vote_name was 0 and no manual case matched)
      TRUE ~ pid_equal_vote_name
    )
  )

eu25games2019 |>
  distinct(meta_pid, .keep_all = T) |>
  count(pid_equal_vote_name2)

# A tibble: 3 × 2
  pid_equal_vote_name2     n
                 <dbl> <int>
1                    0  3697
2                    1 13220
3                   NA 12910

Schematic overview

In [21]:

Code

flowchart TD
    A(Respondents <br> N = 29,827) --> B(No Party ID <br> N = 10,880) 
    A(Respondents <br> N = 29,827) --> C(Party ID <br> N = 18,357)

    B --> D(No Vote <br> N = 3,900)
    B --> E(Vote <br> N = 6,234)

    C --> F(No Vote <br> N = 1,030)
    C --> G(Vote <br> N = 16,917)

    G --> H(Same as PID <br> N = 13,220)
    G --> I(Diff from PID <br> N = 3,697)

flowchart TD
    A(Respondents <br> N = 29,827) --> B(No Party ID <br> N = 10,880) 
    A(Respondents <br> N = 29,827) --> C(Party ID <br> N = 18,357)

    B --> D(No Vote <br> N = 3,900)
    B --> E(Vote <br> N = 6,234)

    C --> F(No Vote <br> N = 1,030)
    C --> G(Vote <br> N = 16,917)

    G --> H(Same as PID <br> N = 13,220)
    G --> I(Diff from PID <br> N = 3,697)

Figures

In [22]:

Code


df_reshaped <- eu25games2019 |>
  group_by(meta_country, der_nopid, der_vote_cat) |>
  count() |>
  ungroup()

df_reshaped

# A tibble: 179 × 4
   meta_country der_nopid der_vote_cat      n
   <chr>            <dbl> <fct>         <int>
 1 Austria              0 Vote reported  3822
 2 Austria              0 Don't know      198
 3 Austria              0 Other            30
 4 Austria              0 NA                6
 5 Austria              1 Vote reported  2622
 6 Austria              1 Don't know      900
 7 Austria              1 Other            78
 8 Austria              1 NA                6
 9 Belgium              0 Vote reported  5064
10 Belgium              0 Don't know      222
# ℹ 169 more rows

Code


total_counts <- df_reshaped |>
  group_by(meta_country, der_nopid) |>
  summarise(n = sum(n), .groups = "drop") |>
  mutate(
    nopid2 = if_else(der_nopid == 1, "No PID", "PID"),
    toprint = paste0(
      nopid2,
      "\n",
      as.character(n)
    ),
    toprint2 = as.character(n)
  )

ggplot(df_reshaped, aes(x = as.factor(der_nopid), y = n, fill = der_vote_cat)) +
  geom_col() +
  geom_text(
    data = total_counts,
    aes(label = toprint2, fill = "black"),
    vjust = if_else(
      total_counts$der_nopid == 1,
      0,
      1
    ),
    size = 3
  ) +
  facet_wrap(~meta_country) +
  labs(y = "N", x = "") +
  scale_x_discrete(labels = c("0" = "PID", "1" = "No PID")) +
  scale_fill_manual(
    values = c(
      "Don't know" = "darkgray",
      "NA" = "black",
      "Other" = "orange",
      "Vote reported" = "gold"
    )
  ) +
  theme_pubr()

Warning in geom_text(data = total_counts, aes(label = toprint2, fill =
"black"), : Ignoring unknown aesthetics: fill

Distribution of Reported Partisan Affiliation and Vote Choice/Intention across included European countries.

Implicit vs explicit partisans distributions

In [23]:

Code


toplot <- eu25games2019 |>
  select(meta_country, der_partisanship, meta_pid) |>
  distinct(meta_pid, .keep_all = T) |>
  filter(!is.na(der_partisanship)) |>
  group_by(meta_country) |>
  count(der_partisanship) |>
  mutate(
    total_n = sum(n),
    prop = n / total_n
  ) |>
  ungroup()

expl_prop <- toplot |>
  filter(der_partisanship == "1_pid_expl") |>
  select(meta_country, expl_prop = prop)

toplot <- toplot |>
  left_join(
    expl_prop,
    by = join_by(meta_country)
  ) |>
  mutate(
    meta_country = fct_reorder(meta_country, expl_prop),
    der_partisanship2 = case_when(
      der_partisanship == "1_pid_expl" ~ "Explicit",
      der_partisanship == "2_pid_impl" ~ "Implicit",
      der_partisanship == "3_nopid" ~ "None"
    )
  )

ggplot(
  toplot,
  aes(x = n, y = meta_country, fill = der_partisanship2)
) +
  geom_bar(
    stat = "identity",
    position = "fill",
    color = "black",
    width = 0.75
  ) +
  scale_fill_manual(
    values = c("white", "#CCCCCC", "black"),
    labels = c(
      bquote("Explicit (" * italic(T)[i] == 1 * ")"),
      bquote("Implicit (" * italic(T)[i] == 0 * ")"),
      expression(paste("None (", italic(T)[i] == phantom(0) * "\u2205", ")"))
    )
  ) +
  scale_x_continuous(labels = percent) +
  labs(
    x = "Share",
    y = "Country",
    fill = bquote("Partisan Type (" * italic(T)[i] * ")")
  ) +
  theme_pubr() +
  theme(
    aspect.ratio = 1 / 1.618034
  )

Distribution of partisan types, by country. Stacked horizontal bars show the within-country share (%) of three partisan types: explicit partisans (respondents who reported a subjective attachement to a party, \(T_i=1\)), implicit partisans (respondents who reported no attachement but did report a vote preference or intention, \(T_i=0\)), and respondents who reported neither (none, \(T_i = \emptyset\)). Percentages sum to 100% within each country, with country samples containing about \(1,100\) respondents each (detailed numbers are reported in appendix section X).

Exploratory analysis of country specific ingroup and outgroup dynamics by partisan type

In [24]:

Code


eu25games2019 <- eu25games2019 |>
  mutate(
    cj_token_logged = log(cj_token + 1)
  )

token_means <- eu25games2019 |>
  filter(meta_game == "dict") |>
  filter(
    !der_outpartisan_comb %in%
      c(
        "97_eunat_expl",
        "97_eunat_impl",
        "98_outnat_expl",
        "98_outnat_impl",
        "99_outnatEU_nopid",
        "99_outnat_nopid"
      ) &
      !is.na(der_outpartisan_comb)
  ) |>
  group_by(meta_country, der_outpartisan_comb) |>
  summarise(
    m = mean(cj_token_logged),
    s = sd(cj_token_logged),
    n = n()
  ) |>
  ungroup()

`summarise()` has grouped output by 'meta_country'. You can override using the
`.groups` argument.

Code


token_means_pooled <- eu25games2019 |>
  filter(meta_game == "dict") |>
  filter(
    !der_outpartisan_comb %in%
      c(
        "97_eunat_expl",
        "97_eunat_impl",
        "98_outnat_expl",
        "98_outnat_impl",
        "99_outnatEU_nopid",
        "99_outnat_nopid"
      ) &
      !is.na(der_outpartisan_comb)
  ) |>
  group_by(der_outpartisan_comb) |>
  summarise(
    m = mean(cj_token_logged),
    s = sd(cj_token_logged),
    n = n()
  ) |>
  ungroup()

token_means_pooled <- token_means_pooled |>
  mutate(meta_country = "Pooled") |>
  select(meta_country, der_outpartisan_comb, m, s, n)

token_means <- token_means |>
  bind_rows(token_means_pooled)


qoi_bycountry <- token_means |>
  group_by(meta_country) |>
  summarise(
    # Explicit Conditions (expl_Type_Stat)

    # IF_expl: Difference (2 - 1)
    expl_IF_diff = m[der_outpartisan_comb == "2_co_expl"] -
      m[der_outpartisan_comb == "1_control_expl"],
    expl_IF_SE = sqrt(
      (s[der_outpartisan_comb == "2_co_expl"]^2 /
        n[der_outpartisan_comb == "2_co_expl"]) +
        (s[der_outpartisan_comb == "1_control_expl"]^2 /
          n[der_outpartisan_comb == "1_control_expl"])
    ),
    expl_IF_CI_LOWER = expl_IF_diff - 1.96 * expl_IF_SE,
    expl_IF_CI_UPPER = expl_IF_diff + 1.96 * expl_IF_SE,

    # OD_expl: Difference (1 - 3)
    expl_OD_diff = m[der_outpartisan_comb == "1_control_expl"] -
      m[der_outpartisan_comb == "3_out_expl"],
    expl_OD_SE = sqrt(
      (s[der_outpartisan_comb == "1_control_expl"]^2 /
        n[der_outpartisan_comb == "1_control_expl"]) +
        (s[der_outpartisan_comb == "3_out_expl"]^2 /
          n[der_outpartisan_comb == "3_out_expl"])
    ),
    expl_OD_CI_LOWER = expl_OD_diff - 1.96 * expl_OD_SE,
    expl_OD_CI_UPPER = expl_OD_diff + 1.96 * expl_OD_SE,

    # AP_expl: Difference (2 - 3)
    expl_AP_diff = m[der_outpartisan_comb == "2_co_expl"] -
      m[der_outpartisan_comb == "3_out_expl"],
    expl_AP_SE = sqrt(
      (s[der_outpartisan_comb == "2_co_expl"]^2 /
        n[der_outpartisan_comb == "2_co_expl"]) +
        (s[der_outpartisan_comb == "3_out_expl"]^2 /
          n[der_outpartisan_comb == "3_out_expl"])
    ),
    expl_AP_CI_LOWER = expl_AP_diff - 1.96 * expl_AP_SE,
    expl_AP_CI_UPPER = expl_AP_diff + 1.96 * expl_AP_SE,

    # Implicit Conditions (impl_Type_Stat)

    # IF_impl: Difference (5 - 4)
    impl_IF_diff = m[der_outpartisan_comb == "5_co_impl"] -
      m[der_outpartisan_comb == "4_control_impl"],
    impl_IF_SE = sqrt(
      (s[der_outpartisan_comb == "5_co_impl"]^2 /
        n[der_outpartisan_comb == "5_co_impl"]) +
        (s[der_outpartisan_comb == "4_control_impl"]^2 /
          n[der_outpartisan_comb == "4_control_impl"])
    ),
    impl_IF_CI_LOWER = impl_IF_diff - 1.96 * impl_IF_SE,
    impl_IF_CI_UPPER = impl_IF_diff + 1.96 * impl_IF_SE,

    # OD_impl: Difference (4 - 6)
    impl_OD_diff = m[der_outpartisan_comb == "4_control_impl"] -
      m[der_outpartisan_comb == "6_out_impl"],
    impl_OD_SE = sqrt(
      (s[der_outpartisan_comb == "4_control_impl"]^2 /
        n[der_outpartisan_comb == "4_control_impl"]) +
        (s[der_outpartisan_comb == "6_out_impl"]^2 /
          n[der_outpartisan_comb == "6_out_impl"])
    ),
    impl_OD_CI_LOWER = impl_OD_diff - 1.96 * impl_OD_SE,
    impl_OD_CI_UPPER = impl_OD_diff + 1.96 * impl_OD_SE,

    # AP_impl: Difference (5 - 6)
    impl_AP_diff = m[der_outpartisan_comb == "5_co_impl"] -
      m[der_outpartisan_comb == "6_out_impl"],
    impl_AP_SE = sqrt(
      (s[der_outpartisan_comb == "5_co_impl"]^2 /
        n[der_outpartisan_comb == "5_co_impl"]) +
        (s[der_outpartisan_comb == "6_out_impl"]^2 /
          n[der_outpartisan_comb == "6_out_impl"])
    ),
    impl_AP_CI_LOWER = impl_AP_diff - 1.96 * impl_AP_SE,
    impl_AP_CI_UPPER = impl_AP_diff + 1.96 * impl_AP_SE,

    # None Condition (none_Type_Stat)

    # OD_none: Difference (7 - 8)
    none_OD_diff = m[der_outpartisan_comb == "7_control_nopid"] -
      m[der_outpartisan_comb == "8_ptycue_nopid"],
    none_OD_SE = sqrt(
      (s[der_outpartisan_comb == "7_control_nopid"]^2 /
        n[der_outpartisan_comb == "7_control_nopid"]) +
        (s[der_outpartisan_comb == "8_ptycue_nopid"]^2 /
          n[der_outpartisan_comb == "8_ptycue_nopid"])
    ),
    none_OD_CI_LOWER = none_OD_diff - 1.96 * none_OD_SE,
    none_OD_CI_UPPER = none_OD_diff + 1.96 * none_OD_SE
  )


tidy_qoi <- qoi_bycountry |>
  # 1. Pivot the data from wide to long format
  pivot_longer(
    # Select all columns except the grouping variable
    cols = -meta_country,

    # 2. Split the column names into three parts: Condition, Type, and Value
    names_to = c("pid_type_raw", "diff_type", ".value"),

    # 3. Define the pattern for separation (Condition_Type_Statistic)
    names_pattern = "(expl|impl|none)_([A-Z]+)_(diff|SE|CI_LOWER|CI_UPPER)$"
    # Explanation:
    # (expl|impl|none) -> Captures the Condition into 'pid_type_raw'
    # ([A-Z]+)         -> Captures the Type (IF, OD, AP) into 'diff_type'
    # (diff|SE|CI_LOWER|CI_UPPER) -> Captures the Statistic into '.value' (creating columns diff, SE, CI_LOWER, CI_UPPER)
  ) |>

  # 4. Clean up the pid_type and rename the 'diff' column
  mutate(
    pid_type = case_match(
      pid_type_raw,
      "expl" ~ "explicit",
      "impl" ~ "implicit",
      "none" ~ "none"
    )
  ) |>
  rename(
    Difference = diff
  ) |>

  # 5. Select and reorder final columns for readability
  select(
    meta_country,
    pid_type,
    diff_type,
    Difference,
    SE,
    starts_with("CI_")
  )


plot_data <- tidy_qoi |>
  mutate(
    country_condition = paste(meta_country, pid_type, sep = " | "),
    country_condition = fct_inorder(country_condition)
  ) |>
  mutate(
    # extract pooled levels
    country_condition = fct_relevel(
      country_condition,
      grep("^Pooled \\|", levels(country_condition), value = TRUE),
      after = Inf # use 0 for top, Inf for bottom
    ),
    country_condition = fct_rev(country_condition),
    diff_type = factor(diff_type, levels = c("IF", "OD", "AP"))
  )


# 2. Create the Plot
ggplot(
  plot_data,
  aes(
    y = country_condition,
    x = Difference,
    shape = pid_type,
    color = meta_country
  )
) +

  # Add the vertical line at zero (to check for significance)
  geom_vline(xintercept = 0, linetype = "dashed", color = "red", alpha = 0.6) +

  # Add the Confidence Intervals (horizontal error bars)
  geom_errorbarh(
    aes(xmin = CI_LOWER, xmax = CI_UPPER),
    height = 0.2,
    linewidth = 0.5
  ) +

  # Add the point estimate
  geom_point(size = 2.5) +

  # Crucial Step: Facet the plot horizontally by the Difference Type (IF, OD, AP)
  facet_wrap(
    ~diff_type,
    ncol = 3,
    scales = "free_x" # Allow x-axis to scale independently for each difference type
  ) +

  # Labels and theming
  labs(
    y = "Country | PID Type",
    x = "Mean Difference (Point Estimate with 95% CI)"
  ) +
  theme_pubr() +
  scale_color_manual(
    values = c(
      rep(
        c(
          "#648FFF",
          "#DC267F",
          "#785EF0",
          "#FE6100",
          "#FFB000"
        ),
        5
      ),
      "black"
    )
  ) +
  theme(
    # Reduce font size for Y-axis labels if they are too long
    axis.text.y = element_text(size = 8, hjust = 0),
    # Ensure facet titles are clear
    strip.text = element_text(face = "bold"),
    legend.position = "none"
  )

Warning: `geom_errorbarh()` was deprecated in ggplot2 4.0.0.
ℹ Please use the `orientation` argument of `geom_errorbar()` instead.

`height` was translated to `width`.

Exploratory token allocation behavior by country and pid type in the dictator game: ingroup favoritism, outgroup derogation and affective polarization. The figure shows mean differences in token allocation with 95% confidence intervals. IF = mean(token2co) - mean(token2control), OD = mean(token2control) - mean(token2out), AP = mean(token2co) - mean(token2out)

In [25]:

Code


eu25games2019 <- eu25games2019 |>
  mutate(
    cj_token_logged = log(cj_token + 1)
  )

token_means <- eu25games2019 |>
  filter(meta_game == "trust") |>
  filter(
    !der_outpartisan_comb %in%
      c(
        "97_eunat_expl",
        "97_eunat_impl",
        "98_outnat_expl",
        "98_outnat_impl",
        "99_outnatEU_nopid",
        "99_outnat_nopid"
      ) &
      !is.na(der_outpartisan_comb)
  ) |>
  group_by(meta_country, der_outpartisan_comb) |>
  summarise(
    m = mean(cj_token_logged),
    s = sd(cj_token_logged),
    n = n()
  ) |>
  ungroup()

`summarise()` has grouped output by 'meta_country'. You can override using the
`.groups` argument.

Code


qoi_bycountry <- token_means |>
  group_by(meta_country) |>
  summarise(
    # Explicit Conditions (expl_Type_Stat)

    # IF_expl: Difference (2 - 1)
    expl_IF_diff = m[der_outpartisan_comb == "2_co_expl"] -
      m[der_outpartisan_comb == "1_control_expl"],
    expl_IF_SE = sqrt(
      (s[der_outpartisan_comb == "2_co_expl"]^2 /
        n[der_outpartisan_comb == "2_co_expl"]) +
        (s[der_outpartisan_comb == "1_control_expl"]^2 /
          n[der_outpartisan_comb == "1_control_expl"])
    ),
    expl_IF_CI_LOWER = expl_IF_diff - 1.96 * expl_IF_SE,
    expl_IF_CI_UPPER = expl_IF_diff + 1.96 * expl_IF_SE,

    # OD_expl: Difference (1 - 3)
    expl_OD_diff = m[der_outpartisan_comb == "1_control_expl"] -
      m[der_outpartisan_comb == "3_out_expl"],
    expl_OD_SE = sqrt(
      (s[der_outpartisan_comb == "1_control_expl"]^2 /
        n[der_outpartisan_comb == "1_control_expl"]) +
        (s[der_outpartisan_comb == "3_out_expl"]^2 /
          n[der_outpartisan_comb == "3_out_expl"])
    ),
    expl_OD_CI_LOWER = expl_OD_diff - 1.96 * expl_OD_SE,
    expl_OD_CI_UPPER = expl_OD_diff + 1.96 * expl_OD_SE,

    # AP_expl: Difference (2 - 3)
    expl_AP_diff = m[der_outpartisan_comb == "2_co_expl"] -
      m[der_outpartisan_comb == "3_out_expl"],
    expl_AP_SE = sqrt(
      (s[der_outpartisan_comb == "2_co_expl"]^2 /
        n[der_outpartisan_comb == "2_co_expl"]) +
        (s[der_outpartisan_comb == "3_out_expl"]^2 /
          n[der_outpartisan_comb == "3_out_expl"])
    ),
    expl_AP_CI_LOWER = expl_AP_diff - 1.96 * expl_AP_SE,
    expl_AP_CI_UPPER = expl_AP_diff + 1.96 * expl_AP_SE,

    # Implicit Conditions (impl_Type_Stat)

    # IF_impl: Difference (5 - 4)
    impl_IF_diff = m[der_outpartisan_comb == "5_co_impl"] -
      m[der_outpartisan_comb == "4_control_impl"],
    impl_IF_SE = sqrt(
      (s[der_outpartisan_comb == "5_co_impl"]^2 /
        n[der_outpartisan_comb == "5_co_impl"]) +
        (s[der_outpartisan_comb == "4_control_impl"]^2 /
          n[der_outpartisan_comb == "4_control_impl"])
    ),
    impl_IF_CI_LOWER = impl_IF_diff - 1.96 * impl_IF_SE,
    impl_IF_CI_UPPER = impl_IF_diff + 1.96 * impl_IF_SE,

    # OD_impl: Difference (4 - 6)
    impl_OD_diff = m[der_outpartisan_comb == "4_control_impl"] -
      m[der_outpartisan_comb == "6_out_impl"],
    impl_OD_SE = sqrt(
      (s[der_outpartisan_comb == "4_control_impl"]^2 /
        n[der_outpartisan_comb == "4_control_impl"]) +
        (s[der_outpartisan_comb == "6_out_impl"]^2 /
          n[der_outpartisan_comb == "6_out_impl"])
    ),
    impl_OD_CI_LOWER = impl_OD_diff - 1.96 * impl_OD_SE,
    impl_OD_CI_UPPER = impl_OD_diff + 1.96 * impl_OD_SE,

    # AP_impl: Difference (5 - 6)
    impl_AP_diff = m[der_outpartisan_comb == "5_co_impl"] -
      m[der_outpartisan_comb == "6_out_impl"],
    impl_AP_SE = sqrt(
      (s[der_outpartisan_comb == "5_co_impl"]^2 /
        n[der_outpartisan_comb == "5_co_impl"]) +
        (s[der_outpartisan_comb == "6_out_impl"]^2 /
          n[der_outpartisan_comb == "6_out_impl"])
    ),
    impl_AP_CI_LOWER = impl_AP_diff - 1.96 * impl_AP_SE,
    impl_AP_CI_UPPER = impl_AP_diff + 1.96 * impl_AP_SE,

    # None Condition (none_Type_Stat)

    # OD_none: Difference (7 - 8)
    none_OD_diff = m[der_outpartisan_comb == "7_control_nopid"] -
      m[der_outpartisan_comb == "8_ptycue_nopid"],
    none_OD_SE = sqrt(
      (s[der_outpartisan_comb == "7_control_nopid"]^2 /
        n[der_outpartisan_comb == "7_control_nopid"]) +
        (s[der_outpartisan_comb == "8_ptycue_nopid"]^2 /
          n[der_outpartisan_comb == "8_ptycue_nopid"])
    ),
    none_OD_CI_LOWER = none_OD_diff - 1.96 * none_OD_SE,
    none_OD_CI_UPPER = none_OD_diff + 1.96 * none_OD_SE
  )


tidy_qoi <- qoi_bycountry |>
  # 1. Pivot the data from wide to long format
  pivot_longer(
    # Select all columns except the grouping variable
    cols = -meta_country,

    # 2. Split the column names into three parts: Condition, Type, and Value
    names_to = c("pid_type_raw", "diff_type", ".value"),

    # 3. Define the pattern for separation (Condition_Type_Statistic)
    names_pattern = "(expl|impl|none)_([A-Z]+)_(diff|SE|CI_LOWER|CI_UPPER)$"
    # Explanation:
    # (expl|impl|none) -> Captures the Condition into 'pid_type_raw'
    # ([A-Z]+)         -> Captures the Type (IF, OD, AP) into 'diff_type'
    # (diff|SE|CI_LOWER|CI_UPPER) -> Captures the Statistic into '.value' (creating columns diff, SE, CI_LOWER, CI_UPPER)
  ) |>

  # 4. Clean up the pid_type and rename the 'diff' column
  mutate(
    pid_type = case_match(
      pid_type_raw,
      "expl" ~ "explicit",
      "impl" ~ "implicit",
      "none" ~ "none"
    )
  ) |>
  rename(
    Difference = diff
  ) |>

  # 5. Select and reorder final columns for readability
  select(
    meta_country,
    pid_type,
    diff_type,
    Difference,
    SE,
    starts_with("CI_")
  )

# 1. Prepare the data for plotting
plot_data <- tidy_qoi |>
  # Create a single factor combining country and condition for the Y-axis
  mutate(
    # Combine country and pid_type into one label (e.g., "Austria | explicit")
    country_condition = factor(paste(meta_country, pid_type, sep = " | ")),

    # Use fct_inorder to maintain the logical order (country-by-country)
    # Then fct_rev reverses the entire factor so the first country appears at the top of the Y-axis
    country_condition = fct_rev(fct_inorder(country_condition))
  ) |>
  # Optional: Clean up diff_type for better facet labels
  mutate(
    diff_type = factor(diff_type, levels = c("IF", "OD", "AP"))
  )


# 2. Create the Plot
ggplot(
  plot_data,
  aes(
    y = country_condition,
    x = Difference,
    shape = pid_type,
    color = meta_country
  )
) +

  # Add the vertical line at zero (to check for significance)
  geom_vline(xintercept = 0, linetype = "dashed", color = "red", alpha = 0.6) +

  # Add the Confidence Intervals (horizontal error bars)
  geom_errorbarh(
    aes(xmin = CI_LOWER, xmax = CI_UPPER),
    height = 0.2,
    linewidth = 0.5
  ) +

  # Add the point estimate
  geom_point(size = 2.5) +

  # Crucial Step: Facet the plot horizontally by the Difference Type (IF, OD, AP)
  facet_wrap(
    ~diff_type,
    ncol = 3,
    scales = "free_x" # Allow x-axis to scale independently for each difference type
  ) +

  # Labels and theming
  labs(
    y = "Country | PID Type",
    x = "Mean Difference (Point Estimate with 95% CI)"
  ) +
  theme_pubr() +
  scale_color_manual(
    values = rep(
      c(
        "#648FFF",
        "#DC267F",
        "#785EF0",
        "#FE6100",
        "#FFB000"
      ),
      5
    )
  ) +
  theme(
    # Reduce font size for Y-axis labels if they are too long
    axis.text.y = element_text(size = 8, hjust = 0),
    # Ensure facet titles are clear
    strip.text = element_text(face = "bold"),
    legend.position = "none"
  )

`height` was translated to `width`.

Exploratory token allocation behavior by country and pid type in the trust game: ingroup favoritism, outgroup derogation and affective polarization. The figure shows mean differences in token allocation with 95% confidence intervals. IF = mean(token2co) - mean(token2control), OD = mean(token2control) - mean(token2out), AP = mean(token2co) - mean(token2out)

In [26]:

Code


eu25games2019 |>
  tabyl(der_partisanship) |>
  adorn_totals()

 der_partisanship      n    percent valid_percent
       1_pid_expl 110142 0.61553852     0.6431199
       2_pid_impl  37418 0.20911387     0.2184840
          3_nopid  23702 0.13246077     0.1383961
             <NA>   7674 0.04288684            NA
            Total 178936 1.00000000     1.0000000

Code


eu25games2019 |>
  tabyl(der_outpartisan_comb) |>
  adorn_totals()

 der_outpartisan_comb      n     percent valid_percent
       1_control_expl  14898 0.083258819   0.086989525
            2_co_expl  30820 0.172240354   0.179958193
           3_out_expl  29366 0.164114544   0.171468277
       4_control_impl   5123 0.028630348   0.029913232
            5_co_impl   1948 0.010886574   0.011374385
           6_out_impl  18465 0.103193321   0.107817262
      7_control_nopid   3236 0.018084678   0.018895026
       8_ptycue_nopid  12878 0.071969866   0.075194731
        97_eunat_expl  19951 0.111497966   0.116494027
        97_eunat_impl   6809 0.038052712   0.039757798
       98_outnat_expl  15107 0.084426834   0.088209877
       98_outnat_impl   5073 0.028350919   0.029621282
    99_outnatEU_nopid   5936 0.033173872   0.034660345
      99_outnat_nopid   1652 0.009232351   0.009646039
                 <NA>   7674 0.042886842            NA
                Total 178936 1.000000000   1.000000000

In [27]:

Code


eu25games2019 |>
  tabyl(der_outpartisan_comb, meta_country) |>
  adorn_totals()

 der_outpartisan_comb Austria Belgium Bulgaria Croatia Czech Republic Denmark
       1_control_expl     517     688      371     505            644     798
            2_co_expl    1091    1442      751    1049           1401    1649
           3_out_expl    1076    1467      775    1018           1259    1636
       4_control_impl     370     208      198     176            124      79
            5_co_impl     169      69       94      58             57      29
           6_out_impl    1277     704      752     660            408     263
      7_control_nopid     119     122      122     223            138      57
       8_ptycue_nopid     499     478      497     842            517     256
        97_eunat_expl     728     971      487     665            882    1089
        97_eunat_impl     463     242      298     251            126      96
       98_outnat_expl     566     724      406     549            662     834
       98_outnat_impl     343     211      206     175            113      73
      99_outnat_nopid      62      55       73     115             68      24
    99_outnatEU_nopid     226     239      232     380            255     113
                 <NA>     156     210      630     774            156     204
                Total    7662    7830     5892    7440           6810    7200
 Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania
     464     598    597     552    582     480     422   697    364       487
     896    1178   1239    1183   1199    1172     875  1563    742       882
     910    1166   1175    1049   1049     913     834  1366    733       903
     244     159    153     240    167     172     266    79    375       346
     122      96     65     104     38      48     124    40     48       114
     807     645    530     842    611     573     954   302   1376      1394
      42     134    166     138    134      95     134   126    119       146
     198     533    681     554    516     389     567   493    582       614
     594     797    837     721    796     733     539   972    473       622
     323     218    193     346    196     182     385   108    488       502
     454     623    598     557    568     536     414   748    364       454
     232     166    169     232    182     159     287    89    365       368
      28      66     81      70     54      50      58    70     85        92
      68     251    284     252    214     174     279   229    282       270
     282     330    168     288    660     240     228   150    492       396
    5664    6960   6936    7128   6966    5916    6366  7032   6888      7590
 Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden
         679    728      628     662      610      604   806    726
        1374   1481     1162    1323     1253     1269  1698   1616
        1382   1332     1220    1352     1242     1114  1581   1486
         152    147      149     325      233      166   199    133
          61     49       40     175       78       56    50     59
         573    647      547    1127      798      598   701    455
         133    107      159     144      188      157   120     99
         523    368      691     625      731      506   410    371
         896    951      788     904      810      750  1055   1024
         246    229      201     416      298      230   248    175
         697    698      588     691      621      541   794    770
         150    170      137     281      225      174   188    144
          72     51       84      79       89       66    61     53
         232    170      288     290      342      249   213    185
         156     60      432     468      264      330   252    228
        7326   7188     7114    8862     7782     6810  8376   7524
 United Kingdom
            689
           1332
           1328
            263
            105
            921
            114
            437
            867
            349
            650
            234
             46
            219
            120
           7674

In [28]:

Code


eu25games2019 |>
  tabyl(der_partisan_type) |>
  adorn_totals() |>
  adorn_rounding() |>
  knitr::kable()

In [29]:

der_partisan_type	n	percent	valid_percent
0	37418	0.2	0.3
1	110142	0.6	0.7
NA	31376	0.2	NA
Total	178936	1.0	1.0

Code


eu25games2019 |>
  tabyl(meta_country, der_partisan_type) |>
  adorn_totals()

In [30]:

   meta_country     0      1   NA_
        Austria  2622   3978  1062
        Belgium  1434   5292  1104
       Bulgaria  1548   2790  1554
        Croatia  1320   3786  2334
 Czech Republic   828   4848  1134
        Denmark   540   6006   654
        Estonia  1728   3318   618
        Finland  1284   4362  1314
         France  1110   4446  1380
        Germany  1764   4062  1302
         Greece  1194   4194  1578
        Hungary  1134   3834   948
        Ireland  2016   3084  1266
          Italy   618   5346  1068
         Latvia  2652   2676  1560
      Lithuania  2724   3348  1518
    Netherlands  1182   5028  1116
         Poland  1242   5190   756
       Portugal  1074   4386  1654
        Romania  2324   4932  1606
       Slovakia  1632   4536  1614
       Slovenia  1224   4278  1308
          Spain  1386   5934  1056
         Sweden   966   5622   936
 United Kingdom  1872   4866   936
          Total 37418 110142 31376

Code


eu25games2019 |>
  filter(meta_country == "Austria") |>
  tabyl(der_partisan_anchor) |>
  adorn_totals() |>
  adorn_rounding()

In [31]:

                                der_partisan_anchor    n percent valid_percent
                 Die Grünen — Die grüne Alternative  834     0.1           0.1
                     EU-Austrittspartei (EUAUS)[og]   42     0.0           0.0
 EUROPA Jetzt – Initiative Johannes Voggenhuber[og]   60     0.0           0.0
                   Freiheitliche Partei Österreichs 1470     0.2           0.2
         KPÖ Plus – European Left, offene Liste[og]   60     0.0           0.0
                  Kommunistische Partei Österreichs   84     0.0           0.0
                                   Liste Peter Pilz   78     0.0           0.0
                         NEOS — Das Neue Österreich  696     0.1           0.1
                                        Piraten[og]   30     0.0           0.0
             Sozialdemokratische Partei Österreichs 1620     0.2           0.2
                        Österreichische Volkspartei 1626     0.2           0.2
                                               <NA> 1062     0.1            NA
                                              Total 7662     1.0           1.0

Code


eu25games2019 |>
  filter(meta_country == "Austria") |>
  tabyl(der_partisan_anchor, der_partisan_type)

In [32]:

                                der_partisan_anchor   0    1  NA_
                 Die Grünen — Die grüne Alternative 390  444    0
                     EU-Austrittspartei (EUAUS)[og]   0   42    0
 EUROPA Jetzt – Initiative Johannes Voggenhuber[og]  60    0    0
                   Freiheitliche Partei Österreichs 504  966    0
                  Kommunistische Partei Österreichs   0   84    0
         KPÖ Plus – European Left, offene Liste[og]  60    0    0
                                   Liste Peter Pilz   0   78    0
                         NEOS — Das Neue Österreich 474  222    0
                        Österreichische Volkspartei 576 1050    0
                                        Piraten[og]   0   30    0
             Sozialdemokratische Partei Österreichs 558 1062    0
                                               <NA>   0    0 1062

Code


eu25games2019 |>
  tabyl(der_partisan_relationship) |>
  adorn_totals() |>
  adorn_rounding()

In [33]:

 der_partisan_relationship      n percent valid_percent
                      None  24326     0.1           0.2
                        Co  51837     0.3           0.3
                       Out  75694     0.4           0.5
                      <NA>  27079     0.2            NA
                     Total 178936     1.0           1.0

Code


eu25games2019 |>
  tabyl(der_partisan_relationship, der_partisan_type)

In [34]:

 der_partisan_relationship     0     1   NA_
                      None  5123 14898  4305
                        Co  3046 48791     0
                       Out 29241 46453     0
                      <NA>     8     0 27071

DESCRIPTIVE SUMMARY STATISTICS

Table respondents by country

In [35]:

Code


eu25games2019 |>
  distinct(meta_pid, .keep_all = T) |>
  tabyl(
    meta_country
  ) |>
  adorn_rounding(2) |>
  adorn_totals() |>
  knitr::kable(col.names = c("Country", "N", "Percent"))

In [36]:

Sample composition by country. Numbers denote respondents.

Country	N	Percent
Austria	1277	0.04
Belgium	1305	0.04
Bulgaria	982	0.03
Croatia	1240	0.04
Czech Republic	1135	0.04
Denmark	1200	0.04
Estonia	944	0.03
Finland	1160	0.04
France	1156	0.04
Germany	1188	0.04
Greece	1161	0.04
Hungary	986	0.03
Ireland	1061	0.04
Italy	1172	0.04
Latvia	1148	0.04
Lithuania	1265	0.04
Netherlands	1221	0.04
Poland	1198	0.04
Portugal	1187	0.04
Romania	1480	0.05
Slovakia	1297	0.04
Slovenia	1135	0.04
Spain	1396	0.05
Sweden	1254	0.04
United Kingdom	1279	0.04
Total	29827	0.99

Table respondents by country and gender

In [37]:

Code


eu25games2019 |>
  distinct(meta_pid, .keep_all = T) |>
  tabyl(
    meta_country,
    q_gender
  ) |>
  adorn_totals() |>
  knitr::kable(
    col.names = c("Country", "Male", "Female", "Other")
  )

In [38]:

Sample composition by country and gender. Numbers denote respondents.

Country	Male	Female	Other
Austria	645	630	2
Belgium	729	574	2
Bulgaria	463	518	1
Croatia	545	694	1
Czech Republic	515	618	2
Denmark	690	508	2
Estonia	331	611	2
Finland	586	567	7
France	562	594	0
Germany	587	597	4
Greece	588	572	1
Hungary	493	492	1
Ireland	481	577	3
Italy	603	569	0
Latvia	415	733	0
Lithuania	462	803	0
Netherlands	642	577	2
Poland	540	658	0
Portugal	593	593	1
Romania	837	641	2
Slovakia	550	746	1
Slovenia	569	566	0
Spain	677	718	1
Sweden	648	602	4
United Kingdom	635	641	3
Total	14386	15399	42

Table respondents by country and age group

In [39]:

Code


eu25games2019 |>
  distinct(meta_pid, .keep_all = TRUE) |>
  group_by(meta_country) |>
  summarise(
    `18-25` = sum(q_age >= 18 & q_age <= 25, na.rm = T),
    `26-35` = sum(q_age >= 26 & q_age <= 35, na.rm = T),
    `36-45` = sum(q_age >= 36 & q_age <= 45, na.rm = T),
    `46-55` = sum(q_age >= 46 & q_age <= 55, na.rm = T),
    `56-65` = sum(q_age >= 56 & q_age <= 65, na.rm = T),
    `66-75` = sum(q_age >= 66 & q_age <= 75, na.rm = T),
    `>76` = sum(q_age >= 76, na.rm = T)
  ) |>
  adorn_totals() |>
  knitr::kable(
    col.names = c(
      "Country",
      "18 to 25",
      "26 to 35",
      "36 to 45",
      "46 to 55",
      "56 to 65",
      "66 to 75",
      "> 75"
    )
  )

In [40]:

Sample composition by country and age group. Numbers denote respondents.

Country	18 to 25	26 to 35	36 to 45	46 to 55	56 to 65	66 to 75	> 75
Austria	144	177	249	263	286	142	0
Belgium	197	132	160	235	333	239	3
Bulgaria	57	195	231	250	212	33	0
Croatia	119	251	272	331	205	47	0
Czech Republic	88	187	227	219	281	126	0
Denmark	132	134	139	208	321	249	4
Estonia	56	168	164	283	265	7	0
Finland	125	178	210	242	259	137	1
France	123	176	242	267	279	51	0
Germany	125	184	200	240	313	117	0
Greece	77	202	376	318	136	34	0
Hungary	51	175	196	166	273	117	1
Ireland	128	216	223	187	169	120	0
Italy	104	196	264	195	294	98	2
Latvia	78	251	231	338	239	6	0
Lithuania	223	311	257	250	212	2	0
Netherlands	149	126	161	234	340	194	3
Poland	200	342	222	187	196	42	0
Portugal	112	260	289	231	218	67	1
Romania	128	339	373	347	202	65	0
Slovakia	139	223	282	290	250	108	1
Slovenia	97	186	246	284	234	72	0
Spain	125	293	355	305	228	66	1
Sweden	124	165	156	234	297	261	3
United Kingdom	121	184	196	234	268	250	1
Total	3022	5251	5921	6338	6310	2650	21

Model DAG

In [41]:

Code


dag <- dagitty(
  "dag {
\"Z1\" [pos=\"0, 1\"]
A [pos=\"0, 2\"]
C [pos=\"2, 1\"]
R [pos=\"1 ,1\"]
T [exposure,pos=\"1 , 2\"]
Y [outcome,pos=\"1 ,0\"]
Z [pos=\"0 ,0\"]
\"Z1\" -> R
A -> R
A -> T
C -> T
C -> Y
R -> Y
T -> R
Z -> Y
}"
)

tidy_dag <- tidy_dagitty(dag)

label_mapping <- tibble(
  name = c("A", "C", "R", "T", "Y", "Z", "Z1"),
  new_label = c(
    "italic(A[i])", # partisan anchor of respondent i
    "italic(C[i])", # respondent confounders
    "italic(R[ri])", # relationship (round r, respondent i)
    "italic(T[i])", # partisan type of respondent i
    "italic(Y[riac])", # tokens (r,i,a,c)
    "italic(Z[r]^{other})", # other randomized cues (round r)
    "italic(Z[r]^{party})" # party cue shown in round r
  )
)

tidy_dag <- tidy_dag |>
  left_join(
    label_mapping,
    by = join_by(name)
  )

ggdag(tidy_dag) +
  geom_dag_node() +
  geom_dag_text(aes(label = new_label), parse = T) +
  geom_dag_edges() +
  theme_dag() +
  theme(
    aspect.ratio = 1 / 1.618034
  )

Directed acyclic graph of the causal data-generating process. Respondents have a partisan anchor \(A_i\), representing the party they feel attached to (explicit partisans) or intend to vote for (implicit partisans). Partisan type \(T_i\) (explicit vs. implicit) is only defined for respondents with a partisan anchor and is therefore a child node of \(A_i\). Each conjoint profile shown in round \(r\) presents randomized attributes: a partisan cue \(Z^{party}_r\) and other attributes \(Z^{other}_r\). The partisan-relationship variable \(R_{ri} = f(A_i, Z^{party}_r)\) determines whether the profile is interpreted as a co-partisan, out-partisan, or neutral for respondent \(i\). Token allocations \(Y_{riac}\) are affected by both \(R_{ri}\) and \(Z^{other}_r\), with the effect of \(R_{ri}\) theorized to depend on \(T_i\). Because \(T_i\) is observational, \(C_i\) denotes potential confounders of both \(T_i\) and \(Y_{ri}\), highlighting the assumptions required for causal interpretation.

Model data

In [42]:

Code


df_modelvars <- eu25games2019 |>
  filter(
    der_conational == "co-national",
    !is.na(der_partisan_type)
  ) |>
  select(
    # outcome Y
    cj_token,
    # vars of interest
    der_partisan_type, # T
    der_partisan_relationship, # R
    # identifiers/hierarchical groupings
    meta_pid,
    der_partisan_anchor,
    meta_country,
    # game variables
    meta_game,
    meta_game_lab,
    # conjoint controls Z
    meta_round,
    meta_wave,
    cj_age_en,
    cj_reli_en,
    cj_class_en,
    cj_sex_en,
    cj_eupos_shown,
    # =========================================================================
    # COVARIATES (C) FOR CAUSAL ISOLATION
    # =========================================================================

    # --- BLOCK 1: Fundamental Political Identity and Ideology (Core Drivers of T & Y) ---
    q_lrpos2, # Left-Right ideological self-placement (Controls for core political position)
    q_eupos2, # EU integration position (Controls for major second dimension position)
    q_econ_nativism, # Attitudes towards economic nativism (Controls for key sociotropic threat perception)
    q_cult_nativism, # Attitudes towards cultural nativism (Controls for key sociotropic identity boundary)
    q_religion_en, # Respondent's religious affiliation (Controls for a major social cleavage/identity)

    # --- BLOCK 2: Anti-System & Populist Attitudes (CRUCIAL CONFOUNDERS for T) ---
    # These variables control for system-level dissatisfaction/cynicism that pushes
    # individuals to reject EXPLICIT partisan identity (T=0) while also driving Y.
    q_satis_demo_country, # Satisfaction with democracy in the country (System trust)
    q_understand_nat_pol, # Political efficacy (national level)
    q_nat_politicians_care, # Political responsiveness (national level)
    q_nat_public_say, # Political efficacy (national level)
    q_understand_eu_pol, # Political efficacy (EU level)
    q_eu_politicians_care, # Political responsiveness (EU level)
    q_eu_public_say, # Political efficacy (EU level)
    q_parties_harm, # Anti-party sentiment ("Parties do more harm than good")
    q_officials_talk_action, # Anti-elitism ("Officials talk too much")
    q_prefer_citizen_rep, # People-centrism ("Prefer citizens to politicians")
    q_people_make_decisions, # People-centrism ("People, not politicians, should decide")
    q_politicians_follow_people, # People-centrism ("Politicians must follow the people's will")
    q_politics_good_evil, # Manichaean worldview ("Politics is good vs. evil")
    q_people_unaware, # System cynicism ("People are unaware")
    q_leaders_educated, # Anti-populism (Elitism)
    q_expert_decisions, # Anti-populism (Expert governance)
    q_listen_other_groups, # Tolerance/Pluralism
    q_democracy_compromise, # Tolerance/Pluralism

    # --- BLOCK 3: Political Engagement & General Identity (Predictors of T and Y) ---
    q_attach_country, # Emotional attachment to one's country
    q_attach_eu, # Emotional attachment to the EU
    q_attach_eur, # Emotional attachment to Europe
    q_interest_pol_country, # Political engagement (national)
    q_interest_pol_eu, # Political engagement (EU)
    q_election_importance, # Political engagement (election importance)

    # --- BLOCK 4: Socioeconomic Status (SES) and Economic Context (Background Predictors) ---
    q_edu, # Education level
    q_perc_class, # Subjective social class
    q_eval_finance_household, # Micro-level economic evaluation
    q_eval_job, # Job/Employment evaluation
    q_eval_econ_country, # Macro-level economic evaluation (country)
    q_eval_econ_eur, # Macro-level economic evaluation (Europe)

    # --- BLOCK 5: Demographics and Fixed Traits (General Controls) ---
    q_gender, # Respondent's gender
    q_age, # Respondent's age
    q_rural_urban, # Residence setting
    q_risk_taking, # General propensity for risk-taking behavior
    q_future_discount # Patience/future discounting trait
  )


na_counts_modelvars1 <- df_modelvars |>
  # 1. Summarise: Calculate the sum of NAs for every column.
  summarise(across(
    .cols = everything(),
    .fns = ~ sum(is.na(.)),
    .names = "na_count_{.col}" # Temporarily rename columns for pivot
  )) |>

  # 2. Pivot: Convert the wide one-row summary into a tall, two-column table.
  pivot_longer(
    cols = everything(),
    names_to = "Variable",
    values_to = "NA_Count"
  ) |>

  # 3. Clean: Remove the temporary prefix for cleaner variable names.
  mutate(Variable = str_remove(Variable, "na_count_")) |>

  # 4. Arrange: Sort the result to see the variables with the most NAs first.
  arrange(desc(NA_Count))


# Remove variables with too many NAs (>10k)
df_modelvars <- df_modelvars |>
  select(
    -q_nat_politicians_care,
    -q_nat_public_say,
    -q_eu_politicians_care,
    -q_eu_public_say,
    -q_prefer_citizen_rep,
    -q_people_make_decisions,
    -q_politicians_follow_people,
    -q_attach_country,
    -q_attach_eu,
    -q_attach_eur,
    -q_election_importance
  )

# Recode dont know answers correctly
df_modelvars <- df_modelvars |>
  mutate(
    q_satis_demo_country = case_when(
      q_satis_demo_country == 5 | q_satis_demo_country == 6 ~ NA,
      .default = q_satis_demo_country
    ),
    q_perc_class = if_else(
      q_perc_class == 6,
      NA,
      q_perc_class
    ),
    q_eval_finance_household = if_else(
      q_eval_finance_household == 6,
      NA,
      q_eval_finance_household
    ),
    q_eval_job = if_else(
      q_eval_job == 6,
      NA,
      q_eval_job
    ),
    q_eval_econ_country = if_else(
      q_eval_econ_country == 6,
      NA,
      q_eval_econ_country
    ),
    q_eval_econ_eur = if_else(
      q_eval_econ_eur == 6,
      NA,
      q_eval_econ_eur
    ),
    q_rural_urban = if_else(
      q_rural_urban == 4,
      NA,
      q_rural_urban
    )
  )

# Categeorical to factors
df_modelvars <- df_modelvars |>
  mutate(
    # categorical to factors
    q_perc_class = factor(
      q_perc_class,
      levels = c(1, 2, 3, 4, 5),
      labels = c(
        "Working class",
        "Lower middle class",
        "Middle class",
        "Upper middle class",
        "Upper class"
      )
    ),
    q_rural_urban = factor(
      q_rural_urban,
      levels = c(1, 2, 3),
      labels = c(
        "Rural area or village",
        "Small or middle sized town",
        "Large town"
      )
    ),
    q_gender = factor(
      q_gender,
      levels = c(1, 2, 3),
      labels = c("Male", "Female", "Other")
    ),
    q_religion_en = as_factor(q_religion_en)
  )

# standardize survey scales
df_modelvars <- df_modelvars |>
  mutate(
    across(
      .cols = c(
        q_lrpos2,
        q_eupos2,
        q_econ_nativism,
        q_cult_nativism,
        q_satis_demo_country,
        q_understand_nat_pol,
        q_understand_eu_pol,
        q_parties_harm,
        q_officials_talk_action,
        q_politics_good_evil,
        q_people_unaware,
        q_leaders_educated,
        q_expert_decisions,
        q_listen_other_groups,
        q_democracy_compromise,
        q_interest_pol_country,
        q_interest_pol_eu,
        q_eval_finance_household,
        q_eval_job,
        q_eval_econ_country,
        q_eval_econ_eur,
        q_risk_taking,
        q_future_discount,
        q_edu,
        q_age
      ), # List all continuous variables to standardize
      .fns = ~ scale(.)[, 1], # Apply the scale() function
      .names = "{.col}_z" # Name the new standardized columns with a '_z' suffix
    )
  )

# Check again how missings have changed
na_counts_modelvars2 <- df_modelvars |>
  # 1. Summarise: Calculate the sum of NAs for every column.
  summarise(across(
    .cols = everything(),
    .fns = ~ sum(is.na(.)),
    .names = "na_count_{.col}" # Temporarily rename columns for pivot
  )) |>

  # 2. Pivot: Convert the wide one-row summary into a tall, two-column table.
  pivot_longer(
    cols = everything(),
    names_to = "Variable",
    values_to = "NA_Count"
  ) |>

  # 3. Clean: Remove the temporary prefix for cleaner variable names.
  mutate(Variable = str_remove(Variable, "na_count_")) |>

  # 4. Arrange: Sort the result to see the variables with the most NAs first.
  arrange(desc(NA_Count))

# Global variable mapping and final df for modelling
df_modelvars <- df_modelvars |>
  select(
    starts_with("cj_"),
    starts_with("der_"),
    starts_with("meta_"),
    ends_with("_z"),
    q_religion_en,
    q_perc_class,
    q_rural_urban,
    q_gender
  )

modelvars_labels <- c(
  "Token" = "cj_token",
  "Conj. Age" = "cj_age_en",
  "Conj. Religion" = "cj_reli_en",
  "Conj. Class" = "cj_class_en",
  "Conj. Gender" = "cj_sex_en",
  "Conj. EU" = "cj_eupos_shown",
  "Type" = "der_partisan_type",
  "Relationship" = "der_partisan_relationship",
  "Anchor" = "der_partisan_anchor",
  "Resp. ID" = "meta_pid",
  "Country" = "meta_country",
  "Game" = "meta_game_lab",
  "Round" = "meta_round",
  "Wave" = "meta_wave",
  "LR Pos" = "q_lrpos2_z",
  "EU Pos" = "q_eupos2_z",
  "Econ. Nativism" = "q_econ_nativism_z",
  "Cult. Nativism" = "q_cult_nativism_z",
  "Satis. Democ." = "q_satis_demo_country_z",
  "Understand Nat. Pol." = "q_understand_nat_pol_z",
  "Understand EU Pol." = "q_understand_eu_pol_z",
  "Parties Harm" = "q_parties_harm_z",
  "Officials Talk/Action" = "q_officials_talk_action_z",
  "Politics Good/Evil" = "q_politics_good_evil_z",
  "People Unaware" = "q_people_unaware_z",
  "Leaders Educated" = "q_leaders_educated_z",
  "Expert Decisions" = "q_expert_decisions_z",
  "Listen Other Groups" = "q_listen_other_groups_z",
  "Democ. Compromise" = "q_democracy_compromise_z",
  "Interest Nat. Pol." = "q_interest_pol_country_z",
  "Interest EU Pol." = "q_interest_pol_eu_z",
  "Eval. HH Finance" = "q_eval_finance_household_z",
  "Eval. Job" = "q_eval_job_z",
  "Eval. Nat. Econ." = "q_eval_econ_country_z",
  "Eval. Eur. Econ." = "q_eval_econ_eur_z",
  "Risk Taking" = "q_risk_taking_z",
  "Future Discount" = "q_future_discount_z",
  "Education (Z)" = "q_edu_z",
  "Age (Z)" = "q_age_z",
  "Religion" = "q_religion_en",
  "Perc. Class" = "q_perc_class",
  "Rural/Urban" = "q_rural_urban",
  "Gender" = "q_gender"
)

Distribution of DV

In [43]:

Code


summary_stats <- df_modelvars %>%
  group_by(meta_game_lab) %>%
  summarise(
    mean_token = mean(cj_token, na.rm = TRUE),
    median_token = median(cj_token, na.rm = TRUE),
    sd_token = sd(cj_token, na.rm = TRUE)
  )


df_modelvars |>
  select(cj_token, meta_game_lab) |>
  ggplot(
    aes(x = log(cj_token + 1), fill = meta_game_lab)
  ) +
  geom_histogram(
    position = position_dodge(),
    color = "black"
  ) +
  # scale_x_continuous(
  #   breaks = 0:10,
  #   labels = 0:10
  # ) +
  scale_fill_manual(
    values = c("white", "gray")
  ) +
  labs(
    x = "Tokens allocated (Y)",
    y = "N",
    fill = "Conjoint Game"
  ) +
  ggpubr::theme_pubr()

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

Distribution of token allocation (Y) by game. Dictator game: \(Mean = 3.41\), \(median = 4\), \(SD = 2.35\). Trust game: \(Mean = 3.48\), \(median = 4\), \(SD = 2.49\)

Distribution of partisan type \(T\) and relationship category \(R\)

In [44]:

Code


df_modelvars |>
  tabyl(der_partisan_type, der_partisan_relationship) |>
  gt()

In [45]:

der_partisan_type	None	Co	Out
0	5123	1948	18465
1	14898	30820	29366

Covariate Distributions

In [46]:

Code


df_modelvars |>
  select(
    der_partisan_type,
    ends_with("_z"),
    q_religion_en,
    q_perc_class,
    q_rural_urban,
    q_gender
  ) |>
  tbl_summary(
    by = der_partisan_type,
    missing = "no",
    type = list(
      q_econ_nativism_z ~ "continuous",
      q_cult_nativism_z ~ "continuous",
      q_satis_demo_country_z ~ "continuous",
      q_understand_nat_pol_z ~ "continuous",
      q_understand_eu_pol_z ~ "continuous",
      q_parties_harm_z ~ "continuous",
      q_officials_talk_action_z ~ "continuous",
      q_politics_good_evil_z ~ "continuous",
      q_people_unaware_z ~ "continuous",
      q_leaders_educated_z ~ "continuous",
      q_expert_decisions_z ~ "continuous",
      q_listen_other_groups_z ~ "continuous",
      q_democracy_compromise_z ~ "continuous",
      q_interest_pol_country_z ~ "continuous",
      q_interest_pol_eu_z ~ "continuous",
      q_eval_finance_household_z ~ "continuous",
      q_eval_job_z ~ "continuous",
      q_eval_econ_country_z ~ "continuous",
      q_eval_econ_eur_z ~ "continuous",
      q_risk_taking_z ~ "continuous",
      q_future_discount_z ~ "continuous"
    )
  ) |>
  modify_header(label = "**Variable**") |>
  as_gt() |>
  tab_options(table.font.size = 10)

In [47]:

Variable	0 N = 25,536¹	1 N = 75,084¹
q_lrpos2_z	-0.08 (-0.45, 0.29)	-0.08 (-0.82, 0.66)
q_eupos2_z	0.08 (-0.68, 0.46)	0.08 (-0.68, 0.84)
q_econ_nativism_z	0.15 (-1.03, 0.74)	0.15 (-1.03, 0.74)
q_cult_nativism_z	0.07 (-1.03, 0.62)	0.07 (-1.03, 0.62)
q_satis_demo_country_z	0.30 (-0.81, 1.40)	0.30 (-0.81, 0.30)
q_understand_nat_pol_z	0.12 (-0.65, 0.12)	0.12 (-0.65, 0.90)
q_understand_eu_pol_z	0.22 (-0.50, 0.22)	0.22 (-0.50, 0.94)
q_parties_harm_z	0.25 (-0.38, 0.88)	0.25 (-0.38, 0.88)
q_officials_talk_action_z	0.41 (-0.33, 1.15)	0.41 (-0.33, 1.15)
q_politics_good_evil_z	-0.14 (-0.76, 0.48)	-0.14 (-0.76, 0.48)
q_people_unaware_z	0.40 (-0.81, 1.00)	-0.20 (-0.81, 1.00)
q_leaders_educated_z	0.39 (-0.32, 1.10)	0.39 (-1.03, 1.10)
q_expert_decisions_z	0.16 (-0.49, 0.80)	0.16 (-0.49, 0.80)
q_listen_other_groups_z	0.18 (-0.74, 1.10)	0.18 (-0.74, 1.10)
q_democracy_compromise_z	-0.29 (-0.29, 0.57)	-0.29 (-0.29, 0.57)
q_interest_pol_country_z	0.06 (-0.60, 0.72)	0.06 (-0.60, 0.72)
q_interest_pol_eu_z	-0.28 (-0.96, 0.39)	0.39 (-0.28, 1.06)
q_eval_finance_household_z	0.01 (-0.99, 1.00)	0.01 (-0.99, 1.00)
q_eval_job_z	0.11 (-0.80, 1.03)	0.11 (-0.80, 0.11)
q_eval_econ_country_z	-0.14 (-1.03, 0.76)	-0.14 (-1.03, 0.76)
q_eval_econ_eur_z	0.07 (-0.94, 1.09)	0.07 (-0.94, 1.09)
q_risk_taking_z	0.11 (-0.56, 0.78)	0.11 (-0.56, 0.78)
q_future_discount_z	-0.17 (-0.84, 0.50)	-0.17 (-0.84, 0.50)
q_edu_z	-0.08 (-0.73, 0.57)	-0.08 (-0.73, 0.57)
q_age_z	-0.11 (-0.92, 0.64)	0.09 (-0.79, 0.91)
q_religion_en
catholic	8,606 (34%)	27,379 (36%)
no religion	9,462 (37%)	25,678 (34%)
protstnt	1,881 (7.4%)	7,570 (10%)
other religion	5,470 (21%)	13,791 (18%)
muslim	114 (0.4%)	664 (0.9%)
q_perc_class
Working class	5,307 (22%)	15,198 (21%)
Lower middle class	4,866 (20%)	13,924 (19%)
Middle class	12,060 (49%)	35,019 (48%)
Upper middle class	2,055 (8.4%)	7,622 (10%)
Upper class	191 (0.8%)	1,310 (1.8%)
q_rural_urban
Rural area or village	5,819 (23%)	17,093 (23%)
Small or middle sized town	9,069 (36%)	28,418 (38%)
Large town	10,573 (42%)	29,298 (39%)
q_gender
Male	10,939 (43%)	39,456 (53%)
Female	14,565 (57%)	35,514 (47%)
Other	32 (0.1%)	114 (0.2%)
¹ Median (Q1, Q3); n (%)

Saving

In [48]:

Code


# save modeldata
save(
  df_modelvars,
  file = here("data", "02_processed", "df_modeldata.RData")
)

CONJOINT RANDOMIZATION CHECKS

Session Info

In [49]:

Code


session_info()

Warning in system2("quarto", "-V", stdout = TRUE, env = paste0("TMPDIR=", :
running command '"quarto"
TMPDIR=C:/Users/Tris/AppData/Local/Temp/RtmpkjsYK1/file5490353246f2 -V' had
status 1

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.2 (2025-10-31 ucrt)
 os       Windows 11 x64 (build 26200)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  English_United States.utf8
 ctype    English_United States.utf8
 tz       Europe/Berlin
 date     2026-01-27
 pandoc   3.6.3 @ c:\\Program Files\\Positron\\resources\\app\\quarto\\bin\\tools/ (via rmarkdown)
 quarto   NA @ C:\\PROGRA~1\\Positron\\RESOUR~1\\app\\quarto\\bin\\quarto.exe

─ Packages ───────────────────────────────────────────────────────────────────
 ! package      * version date (UTC) lib source
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 P graphlayouts   1.2.2   2025-01-23 [?] CRAN (R 4.5.2)
 P gridExtra      2.3     2017-09-09 [?] CRAN (R 4.5.1)
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 * ── Packages attached to the search path.
 P ── Loaded and on-disk path mismatch.

──────────────────────────────────────────────────────────────────────────────

Render Time

In [50]:

Code


end_time <- Sys.time()

rendering_time <- end_time - start_time

message(paste(
  "Document rendered in:",
  round(as.numeric(rendering_time, units = "secs"), 2),
  "seconds.
"
))

Document rendered in: 46.43 seconds.

Code Notebook 3.1

Code Notebook 3.1

Setup

Distribution of implicit and explicit partisans pooled and across countries

Overview of data distribution

The number of respondents

The numer of respondents reporting PID vs not

The number of non-identifiers who report a vote (vs not)

The number of identifiers who report a vote (vs not)

The number of identifiers who reported vote identical to PID

Schematic overview

Figures

Implicit vs explicit partisans distributions

Exploratory analysis of country specific ingroup and outgroup dynamics by partisan type

DESCRIPTIVE SUMMARY STATISTICS

Table respondents by country

Table respondents by country and gender

Table respondents by country and age group

Model DAG

Model data

Distribution of DV

Distribution of partisan type \(T\) and relationship category \(R\)

Covariate Distributions

Saving

CONJOINT RANDOMIZATION CHECKS

Session Info

Render Time